SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
TRK5202	Turkish Language	2	Spring	4	0	4	4

Course Type :	Compulsory
Cycle:	Associate      TQF-HE:5. Master`s Degree      QF-EHEA:Short Cycle      EQF-LLL:5. Master`s Degree
Language of Instruction:	Turkish
Prerequisities and Co-requisities:	N/A
Mode of Delivery:	E-Learning
Name of Coordinator:	Instructor ÖNDER YERAL
Dersin Öğretim Eleman(lar)ı:	Instructor ÖNDER YERAL
Dersin Kategorisi:	Field Specific

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

It is aimed to determine the place and history of the Turkish language among the world languages, its development and to teach the structural features of the Turkish language.

Course Content:

In this course, students are introduced to the various types of prepared speech (lecture, debate, interview, and introducing a work they've read). They are also taught the habit of sound, balanced, independent, and systematic thinking in other areas, as well as the power to research, debate, evaluate, and create. They are taught that reading is an essential need and pleasure, and the habit of reading is fostered.

Course Specific Rules

Watching course materials via e-learning.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:

Knowledge (Described as Theoritical and/or Factual Knowledge.)

1) Have knowledge about world languages ​​and the place of Turkish language among world languages.

2) He/She has knowledge about the historical development of the Turkish language.

Skills (Describe as Cognitive and/or Practical Skills.)

1) Understands the phonetic and structural features of the Turkish Language.

Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)

1) Expresses himself/herself accurately both verbally and in writing.

2) He/She analyzes what she reads and conveys it correctly to the other person.

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	Analyzing an Informative Text (Article) Writing a Summary Making an Outline Cartoon Interpretation Language and Communication Types of Language Written Expression: Writing a Petition Applications	-
2)	Analyzing an Informative Text (Article) Expression: Oral and Written Communication Speech Types of Speech (dialogue, public speaking, meetings) Impromptu Speech Subjective vs. Objective Expression Applications
3)	Analyzing an Informative Text (Essay) Written Expression (planning a text) Written Expression (practice) Oral Expression: Prepared Speech Linguistics: Phonology I	-
4)	Analyzing an Informative Text (Parallel Text Reading) Application: Survey	-
5)	Oral Expression: Debate Linguistics: Phonology II Application: Language Mistakes Analyzing an Informative Text (Oration, Interview) Oral Expression: Debate
6)	Linguistics: Morphology I (words and suffixes) Application: Interview Analyzing an Informative Text (Essay) Poetry Interpretation
7)	Linguistics: Morphology II (derivational and inflectional suffixes) Oral Expression: Panel Discussion Presentation of Term Papers
8)	MİDTERM EXAM
9)	Analyzing an Informative Text (Declaration) Written Expression: News Writing Thinking Method: Comparison (similarities and differences) Linguistics: Languages of the World, Turkish and World Languages
10)	Analyzing a Fictional Text (Short Story) Expression: Thinking Method – Counterargument Development Oral Expression: Panel Discussion Oral Expression: Debate
11)	Written Expression: Paragraph, Types of Paragraphs Linguistics: Turkish Language (historical development, Language and Alphabet Reforms) Analyzing an Informative Text (Scientific Research) Written Expression: Footnotes, References Poetry Analysis Oral Expression: Debate
12)	Expression: Thinking Method – Mind Mapping Oral Expression: Debate Tools and Methods of Developing Ideas: Definition, Exemplification, Comparison, Quotation, Reference, Statistics Travel Writing (Gezi)
13)	Analyzing an Informative Text (Article) Modes of Expression: Expository, Argumentative, Descriptive, Narrative Linguistics: Syntax of Turkish
14)	Letter Writing (personal letters, business letters, official letters) Linguistics: Semantics I Poetry Analysis Expression: Thinking Method – Brainstorming

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Recommended or Required Reading & Other Learning Resources/Tools

Course Notes / Textbooks:

Çotuksöken, Yusuf , Yüksekokullar ve Meslek Yüksekokulları için Türk Dili Çotuksöken, Yusuf, Üniversite Öğrencileri İçin Uygulamalı Türk Dili, İstanbul 2005.

References:

Adalı, Oya, Anlamak ve Anlatmak, İstanbul 2003. Akerson, Fatma Erkman, Dile Genel Bir Bakış, İstanbul 2000. Aksan, Doğan, Her Yönüyle Dil- Ana Çizgileriyle Dilbilim, 3 cilt Ankara 1997-1980. Aksoy, Ömer Asım, Dil Yanlışları, İstanbul 1991. Demircan, Ömer, İletişim ve Dil Devrimi, İstanbul 2000. Ergin, Muharrem, Türk Dil Bilgisi, İstanbul 1968. İmla Kılavuzu, TDK, Ankara 2000 Özdemir, Emin, Sözlü ve Yazılı Anlatım Sanatı-Kompozisyon, İstanbul 1983. Özdemir, Emin, Yazı ve Yazınsal Türler, İstanbul 1981. Özel, Sevgi-Neşe Atabay, Sözcük Türleri I, İstanbul 2003. Özkırımlı, Atilla, Türk Dili-Dil ve Anlatım, İstanbul 2001. Tekin, Talat, Tarih Boyunca Türkçenin Yazımı, Ankara 1997. Tekin, Talat-Mehmet Ölmez, Türk Dilleri-Giriş, İstanbul 1999. Türkçe Sözlük, TDK 2005, Dil Derneği, 2005. Uygur, Nermi, Dilin Gücü, İstanbul 1962. Yazım Kılavuzu, Dil Derneği Ankara 2005.

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)	1	2	3	4	5
Program Öğrenme Çıktıları (PÖÇ)
1) It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results.
2) It explains the principles of algorithm design and develops software for solving problems using at least one programming language.
3) It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data.
4) Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations.
5) They apply natural language processing techniques to text data and develop basic NLP-based applications.
6) It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools.
7) It creates data-driven decision models using decision support systems.
8) It develops optimization models and produces solutions for industrial and sectoral problems.
9) In professional practice, we operate within the framework of ethical principles, data security, and social responsibility.
10) They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results.
2)	It explains the principles of algorithm design and develops software for solving problems using at least one programming language.
3)	It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data.
4)	Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations.
5)	They apply natural language processing techniques to text data and develop basic NLP-based applications.
6)	It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools.	2
7)	It creates data-driven decision models using decision support systems.	1
8)	It develops optimization models and produces solutions for industrial and sectoral problems.	1
9)	In professional practice, we operate within the framework of ethical principles, data security, and social responsibility.
10)	They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course
Lectures
Discussion
Reading
Homework

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course
Midterm
Final Exam
Homework Evaluation

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Homework Assignments	1	% 15.00
Midterms	1	% 35.00
Semester Final Exam	1	% 50.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities	# of Activities per semester	Duration (hour)	Total Workload
Course	14	2	28
Laboratory	0	0	0
Application	1	8	8
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	6	6	36
Presentations / Seminar	0	0	0
Project	0	0	0
Homework Assignments	1	8	8
Total Workload of Teaching & Learning Activities	-	-	80
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	0	0	0
Midterms	1	12	12
Semester Final Exam	1	12	12
Total Workload of Assesment & Evaluation Activities	-	-	24
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			104
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			4